Lower income adults have low health insurance literacy, but there are significant racial/ethnic differences within income levels.

Health insurance literacy is particularly low among the uninsured, but there are significant racial/ethnic differences within the uninsured.

Large Racial and Ethnic Differences in Health Insurance Literacy Signal Need for Targeted Education and Outreach

Sharon K. Long and Dana Goin

February 6, 2014

Overall, health insurance literacy among Americans is low, placing them at a disadvantage when shopping for coverage (Blumberg et al. 2013; Loewenstein et al. 2013). Not understanding key terms related to health insurance prevents consumers from making either fully informed choices among alternative health plans or effective use of the plans they select. Gaps in health insurance literacy are of particular concern for both those purchasing coverage through the health insurance Marketplaces established by the Affordable Care Act (ACA) and those selecting a plan under the ACA’s Medicaid expansion. This brief examines differences in health insurance literacy across three racial/ethnic groups of nonelderly adults (age 18–64)—white non-Hispanic, non-white non-Hispanic, and Hispanic—to identify gaps in understanding that may affect take-up of insurance coverage and health plan selection.

What We Did

This brief draws on data collected in June–July 2013 through the Health Reform Monitoring Survey (HRMS), just before implementation of the Medicaid expansion and health insurance Marketplaces. The survey asked nonelderly adult respondents to rate their confidence in understanding nine terms related to health insurance. Responses of “very confident” or “somewhat confident” for each term were compiled to create composite literacy measures that capture confidence in understanding 1) all terms, 2) the five related to financial aspects of health insurance (premium, deductible, co-payment, coinsurance, and maximum annual out-of-pocket spending) and 3) the four related to nonfinancial aspects of health insurance (provider network, covered services, annual limits on services, and excluded services). We first highlight differences in health insurance literacy across racial/ethnic groups. We then compare literacy levels for the same groups, using regression analysis to adjust for individual differences that potentially affect literacy.1 We estimate models for the entire population. We then estimate models separately by 1) income group (below 138 percent of the federal poverty level [FPL], 138 to 399 percent of FPL, and 400 percent of FPL or above) and 2) insurance status (covered by private insurance at the time of the survey and insured for the prior year, covered by public insurance at the time of the survey and insured for the prior year, and uninsured at any point over the prior year).

What We Found

Although health insurance literacy is low among all three racial/ethnic groups, it is highest for white, non-Hispanic adults and lowest for Hispanic adults.Just over half of white, non-Hispanic adults were somewhat or very confident in understanding all nine health insurance terms, while just over a third of non-white, non-Hispanic adults and fewer than a quarter of Hispanic adults reported confidence in understanding all terms (figure 1)—with both differences significant. Levels of confidence in understanding for all racial/ethnic groups were lower for the financial than the nonfinancial terms. For example, among Hispanic adults, just 22.7 percent were confident in their understanding of financial terms related to health insurance, compared to 30.3 percent for nonfinancial terms.

Although adults in different racial/ethnic groups vary in other characteristics that may affect literacy, it is notable that the differences in health insurance literacy shown in figure 1 persist in the regression-adjusted estimates (table 1). This implies that health insurance literacy is much lower for Hispanic adults than for white, non-Hispanic adults with similar characteristics. For example, if all adults in the sample were white, non-Hispanic, we would predict that 48.6 percent would be somewhat or very confident in their understanding of the health insurance terms, compared to 38.0 percent if they were all non-white, non-Hispanic adults (reflecting a difference of about 10.7 percentage points), and 29.9 percent if they were all Hispanic (reflecting a difference of 18.7 percentage points).

Differences in health insurance literacy across racial/ethnic groups are pronounced, especially among white, non-Hispanic and Hispanic adults with low family incomes. Only about a third of white, non-Hispanic adults with family incomes below 138 percent of FPL reported confidence that they understood all health insurance terms (table 1). While low, this is 10.2 percentage points higher than the level reported by non-white, non-Hispanic adults and 23.9 percentage points higher than that reported by Hispanic adults. Both differences are statistically significant and persist after regression adjustment.

Table 1. Racial/Ethnic Differences in Confidence in Understanding of Health Insurance Terms for Adults Age 18–64, by Family Income

Unadjusted

Regression-adjusted

White, non-Hispanic (%)

Percentage-Point Difference from White, non-Hispanic

White, non-Hispanic (%)

Percentage-Point Difference from White, Non-Hispanic

Non-white, non-Hispanic

Hispanic

Non-white, non-Hispanic

Hispanic

All adults (N=7,068)

Somewhat or very confident
that understand health insurance terms

All terms

52.1

16.2

***

31.0

***

48.6

10.7

***

18.7

***

Financial terms

56.8

15.5

***

34.1

***

53.0

9.1

***

20.9

***

Nonfinancial terms

64.1

16.5

***

33.8

***

60.5

11.2

***

19.4

***

Adults with family income
below 138% of FPL
(N=1,495)

Somewhat or very confident that
understand health insurance terms

All terms

34.7

10.2

***

23.9

***

32.7

7.6

**

20.7

***

Financial terms

39.0

12.4

***

27.3

***

36.8

9.4

***

23.9

***

Nonfinancial terms

44.2

11.3

***

25.4

***

42.1

8.9

**

21.6

***

Adults with family income
138 to 399% of FPL (N=3,033)

Somewhat or very confident that understand health insurance terms

All terms

50.9

13.1

***

22.2

***

49.6

11.7

***

15.8

***

Financial terms

56.8

14.0

***

26.0

***

55.3

12.2

***

19.1

***

Nonfinancial terms

62.7

12.6

***

23.5

***

61.8

12.2

***

17.6

***

Adults with family income
400% of FPL or above (N=2,540)

Somewhat or very confident that understand health insurance terms

All terms

61.0

14.0

***

16.5

**

60.5

12.3

***

11.0

**

Financial terms

64.9

7.7

*

17.6

**

64.2

5.8

11.2

**

Nonfinancial terms

74.2

12.0

***

20.0

***

73.9

12.4

***

14.6

***

Source: Health Reform Monitoring Survey, quarter 2 2013.

Notes: FPL is federal poverty level. Financial terms include premium, deductible, copayments, co-insurance, and maximum annual out-of-pocket spending. Nonfinancial terms include provider network, covered services, annual limits on services, and excluded services. The regression-adjusted estimates are based on a method of recycled predictions, which uses estimated coefficients from regression models to predict health insurance literacy for each adult, alternately assigning the adult to each racial/ethnic category (white non-Hispanic, non-white non-Hispanic, or Hispanic) and leaving all other explanatory variables at their original values. We then average the predictions across all adults in the sample to estimate predicted levels of health literacy for otherwise similar adults in each racial/ethnic group. The covariates included in the regression analysis are age, self-reported health status, gender, marital status, education, family income, homeownership, and urban/rural status. The models for each income category are estimated only within that category, so that the predicted levels for those with family income below 138% of FPL only include individuals in that income category in the sample. Income controls are not used for the models estimated within income categories. Sample sizes for the regression-adjusted estimates vary between imputations and composite variables because of the missing structure of the data. We report the most conservative sample sizes here.

Significant gaps in understanding persist within income groups between white, non-Hispanic adults and adults in the other racial/ethnic groups. For example, 61.0 percent of white, non-Hispanic adults with family incomes above 400 percent of FPL reported confidence in understanding all terms—14.0 percentage points higher than reported by their non-white, non-Hispanic counterparts and 16.5 percentage points higher than reported by their Hispanic counterparts. These differences are statistically significant and also persist after regression adjustment.

Racial/ethnic differences in health insurance literacy are also large across adults with and without insurance coverage, with the gaps particularly large for the uninsured.Differences in health insurance literacy across racial/ethnic groups were most pronounced among those who had been uninsured at some point over the year prior to the survey. While only 35.9 percent of white, non-Hispanic uninsured adults reported confidence in understanding all health insurance terms, this was 12.6 percentage points higher than the confidence reported by non-white, non-Hispanic adults and 25.4 percentage points higher than that reported by Hispanic uninsured adults, both statistically significant. Again, these differences remained after regression adjustment, at 9.3 percentage points and 19.7 percentage points, respectively.

Within the privately and publicly insured with insurance over the year prior to the survey, there were also statistically significant differences in health insurance literacy between white, non-Hispanic adults and adults of other racial/ethnic groups. For example, 57.9 percent of white, non-Hispanic adults with private coverage reported confidence in understanding all terms, 19.8 percentage points higher than that reported by Hispanic adults with private coverage. After regression adjustment, the difference remained large and statistically significant at 14.3 percentage points.

Table 2. Racial/Ethnic Differences in Confidence in Understanding of Health Insurance
Terms for Adults Age 18�64, by Health Insurance Status

Unadjusted

Regression-Adjusted

White, non-Hispanic (%)

Percentage-Point Difference from White, non-Hispanic

White, non-Hispanic (%)

Percentage-Point Difference from White, non-Hispanic

Non-white, non-Hispanic

Hispanic

Non-white, non-Hispanic

Hispanic

All adults (N=7,068)

Somewhat or very confident that understand health insurance terms

All terms

52.1

16.2

***

31.0

***

48.6

10.7

***

18.7

***

Financial terms

56.8

15.5

***

34.1

***

53.0

9.1

***

20.9

***

Nonfinancial terms

64.1

16.5

***

33.8

***

60.5

11.2

***

19.4

***

Private coverage, full-year
insured (N=4,858)

Somewhat or very confident that
understand health insurance terms

All terms

57.9

12.7

***

19.8

***

57.0

10.8

***

14.3

***

Financial terms

62.8

10.1

***

22.5

***

61.9

7.3

***

16.5

***

Nonfinancial terms

70.3

11.7

***

20.2

***

69.6

10.9

***

13.1

***

Public coverage, full-year
insured (N=634)

Somewhat or very confident that understand health insurance terms

All terms

40.5

9.9

*

15.6

***

38.5

8.0

8.9

Financial terms

46.2

12.1

**

22.7

***

43.4

8.8

*

15.1

***

Nonfinancial terms

52.1

11.8

*

20.3

***

49.9

9.6

13.7

**

Ever uninsured in prior
year (N=1,576)

Somewhat or very confident that understand health insurance terms

All terms

35.9

12.6

***

25.4

***

32.6

9.3

***

19.7

***

Financial terms

39.0

13.1

***

27.0

***

35.9

10.2

***

21.1

***

Nonfinancial terms

46.0

13.1

***

28.0

***

43.3

10.5

***

22.4

***

Source: Health Reform Monitoring Survey, quarter 2 2013.

Notes: FPL is federal poverty level. Financial terms include premium, deductible, copayments, co-insurance, and maximum annual out-of-pocket spending. Nonfinancial terms include provider network, covered services, annual limits on services, and excluded services. The regression-adjusted estimates are based on a method of recycled predictions, which uses estimated coefficients from regression models to predict health insurance literacy for each adult, alternately assigning the adult to each racial/ethnic category (white non-Hispanic, non-white non-Hispanic, or Hispanic) and leaving all other explanatory variables at their original values. We then average the predictions across all adults in the sample to estimate predicted levels of health literacy for otherwise similar adults in each racial/ethnic group. The covariates included in the regression analysis are age, self-reported health status, gender, marital status, education, family income, homeownership, and urban/rural status. The models for each coverage category are estimated only within that category, so that the predicted levels for uninsured adults only include those individuals with some period of uninsurance in the year prior to the survey in the sample. Sample sizes for the regression-adjusted estimates vary between imputations and composite variables due to the missing structure of the data. We report the most conservative sample sizes here.

Health insurance literacy varies substantially across the racial/ethnic groups reported on here. This variation persists after controlling for health status and demographic and socioeconomic characteristics—meaning that otherwise similar racial/ethnic minority adults report much lower levels of health insurance literacy than white, non-Hispanic adults. The literacy gaps are particularly large within the low-income and uninsured populations targeted by many of the key provisions of the ACA. Other studies have suggested that the ACA’s coverage expansions have the potential to mitigate racial/ethnic disparities in health insurance coverage, but that high enrollment in Medicaid and the Marketplaces is necessary to achieve progress (Clemans-Cope et al. 2012).

Gaps in understanding key health insurance terms can be expected to have implications for health plan enrollment—both Medicaid enrollment in expansion states and enrollment in individual plans purchased through the Marketplaces. Outreach and education efforts to increase health insurance literacy are crucial in reaching enrollment targets. But to be successful, these efforts must recognize and be tailored to address the current low levels of health insurance literacy. With health insurance literacy levels particularly low for Hispanic adults overall—and even lower for Hispanic adults who are uninsured or have low family incomes—culturally appropriate campaigns and navigators familiar with targeted communities are required to 1) address the population- and locality-specific barriers to health plan enrollment and 2) facilitate connection with specific cultural- and language-appropriate community providers.

This brief is part of a series drawing on the Health Reform Monitoring Survey (HRMS), a quarterly survey of the nonelderly population that is exploring the value of cutting-edge Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. The briefs provide information on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status, as well as timely data on important implementation issues under the ACA. Funding for the core HRMS is provided by the Robert Wood Johnson Foundation, the Ford Foundation, and the Urban Institute.

For more information on the HRMS and for other briefs in this series, visit www.urban.org/hrms.

About the Authors

Sharon K. Long is a senior fellow and Dana Goin is a research associate in the Urban Institute’s Health Policy Center.

Note

1 We adjust for age, self-reported health status, gender, marital status, education, family income, homeownership status, and urban/rural status. In presenting the results, we use the method of recycled predictions to obtain estimates of health insurance literacy for each racial/ethnic group, adjusting for the covariates in the regression models. The models estimated within income categories do not include additional income controls.